As companies mature through their Machine Learning journey, a pattern of “many models” often emerges. In the real world, many problems can be too complex to be solved by a single machine learning model. Whether that be predicting sales for each individual store, building a predictive maintenance model for thousands of oil wells, or tailoring an experience to individual users, building a model for each instance can lead to improved results on many machine learning problems, as opposed to training a single model to make predictions for all instances. However, the infrastructure, procedures and level of automatization required to operate this kind of pattern poses a challenge at all levels.
#BIGTH20 #MLOps #DevOps
Session presented at Big Things Conference 2020 by María Medina, Senior Data Scientist at Microsoft and Hosein Alizadeh, Principal Data Scientist at Microsoft
18th November 2020
Home Edition
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